One of the features in neural computing must be the ability to
adapt to a changeable environment and to recognize unknown objects.
This paper deals with an adaptive optical neural network using Kohonen's
self-organizing feature map algorithm for unsupervised learning. A compact
optical neural network of 64 neurons using liquid crystal televisions
is used for this study. To test the performance of the self-organizing neural
network, experimental demonstrations and computer simulations are provided.
Effects due to unsupervised learning parameters are analyzed. We
show that the optical neural network is capable of performing both unsupervised
learning and pattern recognition operations simultaneously,
by setting two matching scores in the learning algorithm. By using a slower
learning rate, the construction of the memory matrix becomes more organized
topologically. Moreover, the introduction of forbidden regions in
the memory space enables the neural network to learn new patterns without
erasing the old ones.